Pathway Analysis Core

Project Summary

The Pathway Analysis Core provides the ability to identify which specific cell phenotypes are influenced by a toxin, and identify gene regulatory networks associated with toxin exposure. Forming organotypic human tissues in a dish offers the potential to capture the complexity associated with human tissue development and function. However, the increased complexity and phenotypic diversity in organotypic models makes it difficult to understand which cell types a toxin is affecting, and by which specific signaling mechanisms. Bioinformatics and computational tools are required to extract mechanistic information. This Core provides the full suite of bioinformatics tools needed to support the H-MAPs Center objectives. The Pathway Analysis Core is led by Professor Sushmita Roy, who has extensive expertise in bioinformatics approaches, and a particular interest in modeling gene regulatory networks.

Regulatory networks that control context-specific gene expression patterns play a critical role in processing extracellular environmental information to mount accurate downstream responses at the molecular and phenotypic level. Interrogating high-throughput regulatory genomic datasets with pathway and network analysis tools is emerging as a powerful approach to gain a systemic understanding of complex cellular responses. However, such approaches are in their infancy in the toxicology field. The overarching goal of the Pathway Analysis core is to provide an integrated understanding of cellular response to small molecules such as toxins by developing novel network biology tools that can systematically predict regulatory network dynamics controlling cellular response to environmental changes.

We are developing novel computational methods based on machine learning to predict regulatory networks driving global cellular states (Objective 1), identify regulatory circuits that are deferentially wired in different contexts (Objective 2), and applying our approaches to high-throughput datasets generated by the center projects (Objective 3). Successful completion of the objectives of the pathway analysis core will result in a comprehensive collection of network reconstruction and analysis tools that will support each of the proposed Projects, and will be publicly available to researchers affiliated to our center and beyond. The developed tools will be applicable to variety of biological contexts defined by the combination of small molecules (inputs) and tissues or cell types. Application of our tools to genomic datasets collected by various research projects will enable us to identify the common and unique parts of regulatory networks that are activated under different biological contexts. Prioritization of network nodes for downstream functional studies will greatly accelerate discovery of key nodes, including potential biomarkers for a particular biological process.

Fig 1. Inferring regulatory networks by integrating diverse regulatory genomic datasets as structure priors. Xi and Xj denote a regulator (such as a transcription factor) and a target gene respectively. For each candidate edge Xi → Xj, different sources of prior networks can be used. The figure shows three different types of prior networks: ChIP, Motif and Knockout.
Fig. 2 Using a regulatory network module approach, we integrated transcriptomic and proteomic data measuring host response to viruses of different pathogenicities. This module (A) displays a virus-specific differential response and is enriched for known immune response pathways. Inferred regulators for this module were derived by integrating mRNA and protein levels. We also predicted physical interactions among module regulators by integrating protein-protein interactions (B) and identifying a minimal subnetwork (C).
Fig 3.Reconstruction of cell type specific networks can be addressed using tools for three main types of problems: (1) computationally inferring networks from gene expression and epigenomic data, (2) identifying the full complement of context-specific regulatory elements for a gene, and (3) inferring and comparing networks across contexts and cell types.

Prof. Sushmita Roy

Pathway Analysis Core
Principal Investigator

330 North Orchard Street
Madison WI 53715
Room 3168
Website : Click here

Our focus is on developing statistical computational methods to identify the networks driving cellular functions by integrating different types of genome-wide datasets. We will apply our statistical models to multiple proposed organotypic culture models, and ultimately these analyses will be applicable to any organotypic model of interest.